cross_signal_correlate
Discover correlations between different signal types. Example: relationship between ad fill rate and audience attention for QSR venues.
Queries the cross_signal_insights table for pre-computed correlations, or computes ad-hoc correlations from the observation_stream when no pre-computed insight exists.
WHEN TO USE:
Understanding relationships between different sensing signals
Finding which audience behaviors correlate with business outcomes
Discovering hidden patterns (e.g., crowd_energy vs purchase_intent)
Validating hypotheses about audience-venue-time relationships
RETURNS:
data: Correlation analysis with:
signal_a, signal_b: The two signals being correlated
correlation_r: Pearson correlation coefficient (-1 to +1)
correlation_r2: R-squared (proportion of variance explained)
p_value: Statistical significance
sample_count: Number of data points used
effect_size: Cohen's d effect size
confidence_interval_lower, confidence_interval_upper: 95% CI bounds
insight_summary: Human-readable interpretation
metadata: { computation_method, window, filters_applied }
suggested_next_queries: Related correlation analyses to explore
EXAMPLE: User: "Is there a correlation between audience attention and ad fill rate at QSR venues?" cross_signal_correlate({ signal_a: "attention_score", signal_b: "ad_fill_rate", filters: { venue_type: "restaurant_qsr" } })
User: "How does crowd energy relate to purchase intent during lunch hours?" cross_signal_correlate({ signal_a: "crowd_energy", signal_b: "purchase_intent", filters: { daypart: "lunch" } })
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filters | No | Optional filters to narrow the correlation analysis | |
| signal_a | Yes | First signal to correlate (e.g., face_count, attention_score, crowd_energy, emotional_engagement, vehicle_count, noise_level, purchase_intent, ad_fill_rate) | |
| signal_b | Yes | Second signal to correlate against signal_a |